Red light, yellow light, green light

“The rise of artificial intelligence in higher education has forced faculty to make critical decisions: Should AI be allowed in their courses? If so, how much? These aren’t just policy questions — they’re fundamentally about trust, learning and what we want students to actually master. Drawing from the book The Opposite of Cheating: Teaching for Integrity in the Age of AI , faculty should be explicit about what is allowed, what is sometimes allowed and what is not allowed, while explaining the rationale behind these choices to students. Three distinct approaches have emerged in practice: prohibiting AI entirely, permitting it with clear boundaries or embracing it as a learning tool. Recent data from Michigan’s Lansing Community College (LCC) reveals which path faculty are actually choosing. This article comes from the Community College Journal , published by the American Association of Community Colleges . The three approaches Red Light: Prohibiting AI use. Faculty who prohibit AI believe students need to develop skills through their own effort. These policies typically classify AI use as academic dishonesty and appear most often in foundational courses where basic competencies must be mastered without shortcuts. The emphasis is clear: students should be honing critical thinking and personal insight independently. Yellow Light: Permitting AI with boundaries. This middle ground restricts AI to specific uses — perhaps for brainstorming or getting feedback on drafts, but not for final submissions. Faculty spell out when AI is allowed and require students to disclose any AI assistance. This approach recognizes that students likely use these tools anyway, so clear guidance beats blanket bans. Green Light: Encouraging AI integration. Some instructors teach students how to use AI well, framing it as a legitimate academic resource when used ethically. These policies emphasize AI literacy: crafting effective prompts, evaluating AI responses critically, and developing the judgment employers want. Assignments purposely leverage what AI does best while still requiring human analysis and discernment. What faculty at LCC actually chose When Lansing Community College leadership mandated AI policy statements in fall 2025 syllabi, the resulting data — shared with me as a CTE Fellow for Teaching with AI Technology — revealed where faculty really stand: 541 syllabi prohibited AI use entirely (46%) 582 syllabi permitted AI with restrictions (49%) 64 syllabi encouraged AI integration (5%) The “Yellow Light” approach narrowly won, suggesting faculty are cautiously experimenting rather than choosing extremes. What’s striking is that every approach included consequences for improper AI use and potential referral to the Office for Student Compliance. Regardless of the policy chosen, accountability matters. The 46% who chose prohibition and the detection-tool approach face a fundamental challenge: AI detection doesn’t work reliably. These tools produce false positives, can be easily circumvented and create adversarial classroom dynamics. The real solution lies in prevention through assessment design, not after-the-fact policing. When assignments require personal reflection, local knowledge, or multi-stage processes, AI offers no genuine shortcuts. Authentic assessment — where learning happens in the process, not just the final product — builds trust rather than suspicion. This approach is especially vital in online courses and for community college students who need partnership, not surveillance. Five critical questions for AI policy To help faculty make informed decisions, especially for online courses, consider these essential questions: Learning objectives: What specific skills do I want students to develop, and how might AI support or hinder this? For writing courses focused on argumentation, AI might undermine learning. For data analysis courses, AI might be invaluable. Professional preparation: How will my students encounter AI in their careers, and how can my course prepare them? Engineering students will use AI tools daily; counseling students need to understand AI’s limitations in human services. Assessment design: Can I design assessments that are valuable learning experiences regardless of AI availability? Focus on assignments where learning happens in the process: collaborative projects, personal reflections or iterative challenges requiring continuous human judgment. Student support: How might my policy impact students with different backgrounds or learning needs? Consider whether AI tools might support students with disabilities or non-native speakers without creating unfair advantages. Sustainability: Can I realistically implement my policy without creating an adversarial atmosphere? Avoid policies requiring constant surveillance. Focus on engaging students as partners in maintaining integrity. Making it work in practice If you prohibit AI, create assignments tied to students’ unique experiences or multi-step processes only humans would navigate. Make your reasoning explicit. If you allow AI conditionally, be crystal clear about boundaries. Require disclosure and scaffold practice so students build ethical habits. If you encourage AI, explicitly teach about it — from prompt engineering to recognizing bias. As one LCC student explained, “I believe students should be taught how to use AI ethically, not as a shortcut, but as a learning tool.” Design assignments showcasing what humans and machines can do together, and model good AI usage yourself. The bottom line The policy you choose matters less than why you choose it and how you implement it. What works at a research university may not work at a community college where students juggle work, family and coursework. Online learning adds another layer of complexity, requiring even more intentional trust-building and communication. The LCC data show faculty gravitating toward guided experimentation rather than extremes. This middle path — setting boundaries while acknowledging AI’s reality — may be the most sustainable approach for community colleges where flexibility and student support are paramount. This aligns with student perspectives, too. As one LCC student put it, “GenAI is not perfect, but when used wisely, it can make education more accessible and creative. As students, our job is to use it to learn, not to replace learning.” As one researcher noted, “The opposite of cheating is learning.” That’s what these decisions are really about: creating environments where students engage meaningfully with course material, whether AI is part of that process or not. The goal isn’t catching cheaters; it’s fostering learning communities where thoughtful technology use (or non-use) serves student growth. At community colleges, especially, where students often represent the most diverse educational backgrounds and life circumstances, our AI policies must be clear, fair and rooted in what students actually need to succeed. The red, yellow and green lights aren’t about being permissive or restrictive — they’re about being intentional. The post Red light, yellow light, green light first appeared on Community College Daily .

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